We propose spex-LVM, a flexible and efficient latent variable model capable of utilizing prior information of well-known biomedical gene set annotations to guide the inference of explainable sources of variation in scRNA-seq data.
More specifically, spex-LVM learns interpretable factors that capture meaningful subpopulations of cells or patients, automatically tagged with the biomedical pathway characterizing the corresponding axis of variation. We account for unwanted technical noise[1]} by explicitly modeling irrelevant sources of variation as additional factors that capture perturbations affecting large groups of features. In addition, our approach considers potentially noisy annotations and provides refined alternatives supported by the data.
Finally, our method provides identifiable latent factors. Experiments show that the order of the learned factors matches the order of the provided annotations, thereby circumventing a known drawback inherent to the conventional factor analysis[2]}, and yielding consistent and reproducible results. We should note that our method is not limited to biomedical data analysis, and it can be easily applied to other domains where partial information on the latent structure is available.
